Turbo-BLAST: performance evaluation in correlated Rayleigh-fading environment

Theoretical investigations of spatially correlated multitransmit and multireceive (MTMR) links show that not only independently and identically distributed links, but also spatially correlated links can offer linear capacity growth with increasing number of transmit and receive antennas. We explore the suitability of the turbo-BLAST architecture in correlated Rayleigh-fading MTMR environments. In particular, for an MTMR system with a large number of receive antennas, a near optimal performance can be achieved by the turbo-BLAST architecture in spatially and temporarily correlated Rayleigh-fading environments. The performance of turbo-BLAST, in terms of both bit-error rate and spectral efficiency, is analyzed empirically in indoors and correlated outdoor environments.     

Analysis of Turbulence and Convective Inertia in a Water-Lubricated Tilting-Pad Journal Bearing Using Conventional and CFD Approaches

The influence of turbulence and convective fluid inertia in a water-lubricated journal bearing was investigated using two types of models: a “conventional” solution based on traditional lubrication theory (Reynolds equation) and a more rigorous computational fluid dynamics (CFD) program containing a full Navier-Stokes solution. The calculations reveal that turbulence accounts for around 50% of the load capacity in the water-lubricated bearing studied, highlighting the importance of accurate characterization of turbulence in such applications. Convective inertia, also referred to as transport inertia because it depends only on the spatial parameters within the film rather than time-dependent journal motions, was found to lower the static film pressures (load capacity) by about 6% compared to an inertialess solution.

Hydrodynamic pressures calculated by the conventional Reynolds solution were initially about 30% lower than those of the more rigorous CFD model for the water-lubricated bearing operating in the turbulent regime. The mesh spacing of the conventional model was refined and a method was developed to adjust the turbulence model within the Reynolds solution as a function of the pivot Reynolds number. These refinements brought the calculated bearing load capacities and power losses of the conventional Reynolds model into better agreement with those of the CFD model for a broad range operating conditions.     

Neural network-based receiver for wireless communications

A novel approach, based on neural networks, for the design of a receiver for TDMA wireless communications is described. The receiver utilises three functional blocks: time-frequency analysis, feature extraction, and pattern classification. Computer simulation results are presented comparing the performance of the new receiver against a conventional MSK receiver for a Rayleigh fading multipath channel. The results show that the new receiver is capable of achieving a performance comparable to that of the MSK receiver without the regular transmission of a training sequence     

Syntactic Modeling and Signal Processing of Multifunction Radars: A Stochastic Context-Free Grammar Approach

Multifunction radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that ldquospeakrdquo a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate radar's policies of operation, and a maximum likelihood parameter estimator to infer the radar parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger radar signal processing problem that is often encountered in electronic warfare applications-the problem of the estimation of the level of threat that a radar poses to each individual target at any point in time.     

Modular learning strategy for signal detection in a nonstationaryenvironment

We describe a novel modular learning strategy for the detection of a target signal of interest in a nonstationary environment, which is motivated by the information preservation rule. The strategy makes no assumptions on the environment. It incorporates three functional blocks: (1) time-frequency analysis, (2) feature extraction, and (3) pattern classification, the delineations of which are guided by the information preservation rule. The time-frequency analysis, which is implemented using the Wigner-Ville distribution (WVD), transforms the incoming received signal into a time-frequency image that accounts for the time-varying nature of the received signal's spectral content. This image provides a common input to a pair of channels, one of which is adaptively matched to the interference acting alone, and the other is adaptively matched to the target signal plus interference. Each channel of the receiver consists of a principal components analyzer (for feature extraction) followed by a multilayer perceptron (for feature classification), which are implemented using self-organized and supervised forms of learning in feedforward neural networks, respectively. Experimental results based on real-life radar data are presented to demonstrate the superior performance of the new detection strategy over a conventional detector using constant false-alarm rate (CFAR) processing. The data used in the experiment pertain to an ocean environment, representing radar returns from small ice targets buried in sea clutter; they were collected with an instrument quality coherent radar and properly ground truthed     

A novel model-based hearing compensation design using a gradient-free optimization method

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed, to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.     

Experimental characterisation of diffuse multipath at 10.2 GHz using method of multiple windows

Thomson's multiple window method of spectrum estimation is applied to the problem of diffuse multipath characterisation in a low angle tracking radar environment. The method gives optimum power spectrum estimates in both the frequency and wavenumber domains. The results presented are based on data collected by means of a 32-element sampled aperture antenna system operating at 10.2 GHz.<>     

Optimally adaptive transform coding

The optimal linear block transform for coding images is well known to be the Karhunen-Loeve transformation (KLT). However, the assumption of stationarity in the optimality condition is far from valid for images. Images are composed of regions whose local statistics may vary widely across an image. While the use of adaptation can result in improved performance, there has been little investigation into the optimality of the criterion upon which the adaptation is based. In this paper we propose a new transform coding method in which the adaptation is optimal. The system is modular, consisting of a number of modules corresponding to different classes of the input data. Each module consists of a linear transformation, whose bases are calculated during an initial training period. The appropriate class for a given input vector is determined by the subspace classifier. The performance of the resulting adaptive system is shown to be superior to that of the optimal nonadaptive linear transformation. This method can also be used as a segmentor. The segmentation it performs is independent of variations in illumination. In addition, the resulting class representations are analogous to the arrangement of the directionally sensitive columns in the visual cortex.